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Millimeter-wave (MMW) is a probable technology for the future cellular systems. Its main challenge is achieving sufficient operating link margin, and directional beamforming with large antenna arrays may be a viable approach. With bandwidths on the order of gigahertz, high-resolution analog-to-digital converters are a power consumption bottleneck. One solution is to employ an hybrid implementation,...
This paper presents a memetic approach combining the Modified Artificial Bee Colony algorithm (MABC) and the Hooke-Jeeves method to improve its performance to solve constrained numerical optimization problems. The operator used by the employed bees was modified in such a way that more diverse solutions are generated. For constraint handling, the set of feasibility rules used in the original MABC was...
Large-scale multiple-input multiple-output (LS-MIMO) is considered as a promising key technology for future 5G wireless communications due to its very high spectrum and energy efficiency. However, one challenging problem to achieve these benefits is a practical signal detection algorithm in the uplink. In this paper, we propose a low-complexity near-optimal signal detection algorithm using the conjugate...
We study the problem of utility fair bandwidth allocation via association control in wireless mesh networks (WMNs). We formulate an optimization problem that achieves an utility fairness objective and jointly considers user association and resource allocation in WMNs. As the problem is NP-hard, we propose an approximation algorithm whose solution is very close to the optimal one, which is demonstrated...
Bit-parallel pattern matching encodes calculated values in bit arrays. This approach gains its efficiency by performing multiple updates within a machine word. An important parameter is therefore the machine word size (e.g. 32 or 64 bits). With the increasing length of vector registers, the efficient mapping of bit-parallel pattern matching algorithms onto modern high performance computing architectures...
Nonnegative matrix factorization (NMF) has been widely used to reduce dimensionality of data in image processing and various applications. Incorporating the geometric structure into NMF, graph regularized nonnegative matrix factorization (GNMF) has shown significant performance improvement in comparison to conventional NMF. However, both NMF and GNMF require the data matrix to reside in the memory,...
In the compressive sampling theory, a small number of random linear projections of a sparse or compressible signal have contained sufficient information and the original signal can be accurately reconstructed by taking advantage of modern optimization algorithms. We proposed an approximate l0 norm based signal reconstruction algorithm in this paper. It not only can convert the classical constrained...
The Fréchet distance is a well-studied and very popular measure of similarity of two curves. Many variants and extensions have been studied since Alt and Godau introduced this measure to computational geometry in 1991. Their original algorithm to compute the Fréchet distance of two polygonal curves with n vertices has a runtime of O(n^2 log n). More than 20 years later, the state of the art algorithms...
This paper proposes an efficient method for locating multiple off grid targets, whose responses lies in a high-dimensional space, in the presence of Doppler ambiguities. The detection and localization method is based on Orthogonal Matching Pursuit (OMP), a sparse linear decomposition technique for solving Compressed Sensing (CS) problems, combined with a gradient algorithm that adapts the grid to...
We consider a cloud as a cluster of processors holding each a large XML tree. We present a statistical representation which can be built online on each processor and allows to approximate boolean, unary and Aggregation queries. The main result of the paper shows how these statistics can be efficiently Reduced to a master node of the cloud. We obtain an approximation of the global tree structure built...
In this article, we introduce the quantum adiabatic computation to the research field of quantum operator learning. Compared with existing conventional optimization approaches, the adiabatic algorithm ensures to reach the global optimal solution, and thus avoids the local minimum problem. The performance of the experiments on two tasks indicates the feasibility and potentiality of this novel method...
We present here new results and algorithms for the Linear Arrangement Problem (LAP). We first propose a new lower bound, which links LAP with the Max Cut Problem, and derive a LIP model as well as a branch/bound algorithm for the general case. Then we focus on the case of interval graphs: we first show that our lower bound is tight for unit interval graphs, and derive an efficient polynomial time...
In this paper we present an improvement of the algorithm based on recursive de Casteljau subdivision over an n-dimensional bounded domain (simplex or box). The modification consists of a novel end condition and a way of calculation the root in subdomain. Both innovations are based on linear approximation of polynomials in a system. This improvement results in that our approach takes almost half of...
This work presents a parallel implementation of the Parareal method using Portable Extensible Toolkit for Scientific Computation (PETSc). An optimal control problem of a parabolic partial differential equation with known boundary conditions and initial state is solved, where the minimized cost function relates the controller v usage and the approximation of the solution y to an optimal known function...
This paper presents an incremental sampling-based approach for trajectory imitation in cluttered environments using the RRT* algorithm. Inspired by the discrete Laplace-Beltrami operator the underlying distance metric is based upon the difference from a reference trajectory through a quadratic distance term incorporating velocity and acceleration deviations along the trajectory. Mathematically-backed...
Orthonormal Basis Function (OBF) models are used to define stable fixed-poles infinite impulse response filter structures that allow to incorporate knowledge about the resonant characteristics of a stable, causal and linear system. In the approximation of a room impulse response, OBF models can include knowledge about the room resonances as a set of poles, which appear nonlinearly in the structure...
In Recommender system we have similarity search as a key part for making efficient recommendations. Similarity search have always been a tough task in a high dimensional space. Locality Sensitive Hashing which is most suitable for extracting data in a high dimensional data (Multimedia data). The Idea of locality sensitive hashing is that it decreases the high dimensional data to low dimensions using...
Minimum mean square error (MMSE) signal detection is near-optimal for uplink multi-user large-scale MIMO systems with hundreds of antennas at the base station, but involves matrix inversion with high complexity. In this paper, we first prove that the filtering matrix of the MMSE algorithm in large-scale MIMO is symmetric positive definite, based on which we propose a low-complexity signal detection...
The scarcity of radio resources coupled with the high data rate demands in the last few years, induced incremental relaying techniques one of the most promising technologies. This is simply because it allows reaping spatial diversity and saving the channel resources at the same time. In this contribution, we make use of the Decode and Forward incremental relaying technique in OFDM cooperative systems...
The paper analyzes the problem of predicting the outcome of elections (how many votes each candidate is going to get), given an imperfect information on the preferences of the voters. We assume that we have a fixed prior on the preferences of each voter for each candidate. We have used two naive algorithms which predict the votes obtained by each candidate in an election. The algorithms are fast and...
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